Adversarial machine learning in cybersecurity: Mitigating evolving threats in AI-powered defense systems
1 Department of Information and communication science, Ball state university, Muncie Indiana, USA.
2 Mechanical Engineering, University of Nigeria Nsukka Nigeria.
3 Department of Cybersecurity, Eastern Illinois University, Charleston, Illinois, United States.
4 IA Technology Risk and Cybersecurity, Goldman Sachs, New York, USA.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2023, 10(02), 309-325.
Article DOI: 10.30574/wjaets.2023.10.2.0294
Publication history:
Received on 12 October 2023; revised on 26 October 2023; accepted on 29 November 2023
Abstract:
The increasing integration of artificial intelligence (AI) in cybersecurity has enhanced the ability to detect and mitigate cyber threats in real-time. However, adversarial machine learning (AML) has emerged as a significant challenge, enabling attackers to manipulate AI models and bypass security measures. This study explores the evolving landscape of AML threats and the vulnerabilities they introduce to AI-powered defense systems. The research identifies key adversarial attack techniques, including evasion, poisoning, model inversion, and model extraction, which threaten the integrity and effectiveness of AI-driven cybersecurity mechanisms. This study evaluates various mitigation strategies to address these threats, such as adversarial Training, model hardening, defensive Distillation, and hybrid AI approaches. Through experimental analysis, we assess the robustness of AI defense systems under adversarial attack and measure their effectiveness using key performance metrics, including model accuracy, false positive rates, and computational efficiency. The findings indicate that while adversarial Training improves model resilience, adaptive attack techniques continue to challenge existing defenses, necessitating continuous advancements in cybersecurity frameworks. This research highlights the need for a multi-layered security approach that integrates AI-based anomaly detection, human-AI hybrid security models, and adaptive learning techniques to counter adversarial threats effectively. Additionally, it discusses the broader implications of AML in cybersecurity, including policy considerations, ethical concerns, and future research directions. The study recommends strategies for enhancing AI-powered cyber defense systems to maintain security, reliability, and resilience against evolving adversarial threats.
Keywords:
Adversarial Machine Learning; AI-Powered Cybersecurity; Adversarial Attacks; Intrusion Detection Systems (Ids); Cyber Threat Intelligence
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This paper has received BEST PAPER AWARD of Volume 10 - Issue 2 (November - December 2023).
Copyright information:
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0